Predeployment predictors of psychiatric disorder‐symptoms and interpersonal violence during combat deployment

Rosellini, Anthony J.; Stein, Murray B.; Benedek, David M.; Bliese, Paul D.; Chiu, Wai Tat; Hwang, Irving; Monahan, John; Nock, Matthew K.; Sampson, Nancy A.; Street, Amy E.; Zaslavsky, Alan M.; Ursano, Robert J.; Kessler, Ronald C.

doi:10.1002/da.22807

Cited by 23 publications

(16 citation statements)

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“…The current state of research using ML in PTSD resilience research is summarized in two recent review articles [19,20]. In military context, a few major studies identifying risk factors using ML for predicting suicide [21], psychiatric disorders [22], and PTSD in military personnel [14,23,24] have been conducted. Advanced computational methodology has demonstrated that nonlinear and highly interacting combinations of heterogeneous risk factors are most predictive [23], despite the fact that such complex probabilistic information is difficult to grasp.…”

Section: Introductionmentioning

confidence: 99%

Pre-deployment risk factors for PTSD in active-duty personnel deployed to Afghanistan: a machine-learning approach for analyzing multivariate predictors

et al. 2020

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Active-duty Army personnel can be exposed to traumatic warzone events and are at increased risk for developing posttraumatic stress disorder (PTSD) compared with the general population. PTSD is associated with high individual and societal costs, but identification of predictive markers to determine deployment readiness and risk mitigation strategies is not well understood. This prospective longitudinal naturalistic cohort study-the Fort Campbell Cohort study-examined the value of using a large multidimensional dataset collected from soldiers prior to deployment to Afghanistan for predicting postdeployment PTSD status. The dataset consisted of polygenic, epigenetic, metabolomic, endocrine, inflammatory and routine clinical lab markers, computerized neurocognitive testing, and symptom self-reports. The analysis was computed on activeduty Army personnel (N = 473) of the 101st Airborne at Fort Campbell, Kentucky. Machine-learning models predicted provisional PTSD diagnosis 90-180 days post deployment (random forest: AUC = 0.78, 95% CI = 0.67-0.89, sensitivity = 0.78, specificity = 0.71; SVM: AUC = 0.88, 95% CI = 0.78-0.98, sensitivity = 0.89, specificity = 0.79) and longitudinal PTSD symptom trajectories identified with latent growth mixture modeling (random forest: AUC = 0.85, 95% CI = 0.75-0.96, sensitivity = 0.88, specificity = 0.69; SVM: AUC = 0.87, 95% CI = 0.79-0.96, sensitivity = 0.80, specificity = 0.85). Among the highest-ranked predictive features were pre-deployment sleep quality, anxiety, depression, sustained attention, and cognitive flexibility. Blood-based biomarkers including metabolites, epigenomic, immune, inflammatory, and liver function markers complemented the most important predictors. The clinical prediction of post-deployment symptom trajectories and provisional PTSD diagnosis based on pre-deployment data achieved high discriminatory power. The predictive models may be used to determine deployment readiness and to determine novel pre-deployment interventions to mitigate the risk for deployment-related PTSD.

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Section: Introductionmentioning

confidence: 99%

Pre-deployment risk factors for PTSD in active-duty personnel deployed to Afghanistan: a machine-learning approach for analyzing multivariate predictors

et al. 2020

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Section: Introductionmentioning

confidence: 99%

“…In the military context, Kessler and colleagues [ 18 ] applied ML techniques to predict suicide after hospitalization with psychiatric diagnoses in service members, whereas Rosellini and colleagues [ 19 ] used ML to predict postdeployment psychiatric disorder symptoms and interpersonal violence during deployment based on predeployment characteristics. Both studies found that ML provides relatively accurate prediction of the targeted outcomes, with the best performing predictive models in the study by Rosellini et al [ 19 ] significantly outperforming logistic regression models. In an earlier study by our own group, we applied a specific ML algorithm, namely support vector machine (SVM), in a cohort study with Danish soldiers deployed to Afghanistan with the aim of predicting PTSD symptomatology 2.5 years after deployment based on predeployment and early postdeployment characteristics [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Applicability of an Automated Model and Parameter Selection in the Prediction of Screening-Level PTSD in Danish Soldiers Following Deployment: Development Study of Transferable Predictive Models Using Automated Machine Learning

Karstoft¹,

Tsamardinos²,

Eskelund³

et al. 2020

JMIR Med Inform

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Background Posttraumatic stress disorder (PTSD) is a relatively common consequence of deployment to war zones. Early postdeployment screening with the aim of identifying those at risk for PTSD in the years following deployment will help deliver interventions to those in need but have so far proved unsuccessful. Objective This study aimed to test the applicability of automated model selection and the ability of automated machine learning prediction models to transfer across cohorts and predict screening-level PTSD 2.5 years and 6.5 years after deployment. Methods Automated machine learning was applied to data routinely collected 6-8 months after return from deployment from 3 different cohorts of Danish soldiers deployed to Afghanistan in 2009 (cohort 1, N=287 or N=261 depending on the timing of the outcome assessment), 2010 (cohort 2, N=352), and 2013 (cohort 3, N=232). Results Models transferred well between cohorts. For screening-level PTSD 2.5 and 6.5 years after deployment, random forest models provided the highest accuracy as measured by area under the receiver operating characteristic curve (AUC): 2.5 years, AUC=0.77, 95% CI 0.71-0.83; 6.5 years, AUC=0.78, 95% CI 0.73-0.83. Linear models performed equally well. Military rank, hyperarousal symptoms, and total level of PTSD symptoms were highly predictive. Conclusions Automated machine learning provided validated models that can be readily implemented in future deployment cohorts in the Danish Defense with the aim of targeting postdeployment support interventions to those at highest risk for developing PTSD, provided the cohorts are deployed on similar missions.

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“…3 4 5 Evidently, the combination of BFS with sophisticated analytical methods such as data mining and Machine learning (ML) has provided new insights in the military regarding musculoskeletal injury, 6 post-traumatic stress disorder 7 and interpersonal violence prediction. 8 However, only few studies exist 9 10 in relation to the role of BFS for movement recognition in active military populations and solely characterize Swiss Army recruits using commercially available accelerometers. Physical conditioning training in military varies considerably among countries, even within military alliances such as NATO, metabolic and neuromuscular adaptations can accordingly vary among different armies, with subsequent effects on movement patterns.…”

Section: Introductionmentioning

confidence: 99%

Employing body-fixed sensors and machine learning to predict physical activity in military personnel

et al. 2020

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IntroductionThis was a feasibility pilot study aiming to develop and validate an activity recognition system based on a custom-made body-fixed sensor and driven by an algorithm for recognising basic kinetic movements in military personnel. The findings of this study are deemed essential in informing our development process and contributing to our ultimate aim which is to develop a low-cost and easy-to-use body-fixed sensor for military applications.MethodsFifty military participants performed a series of trials involving walking, running and jumping under laboratory conditions in order to determine the optimal, among five machine learning (ML), classifiers. Thereafter, the accuracy of the classifier was tested towards the prediction of these movements (15 183 measurements) and in relation to participants’ gender and fitness level.ResultsRandom forest classifier showed the highest training and validation accuracy (98.5% and 92.9%, respectively) and classified participants with differences in type of activity, gender and fitness level with an accuracy level of 83.6%, 70.0% and 62.2%, respectively.ConclusionsThe study showed that accurate prediction of various dynamic activities can be achieved with high sensitivity using a low-cost easy-to-use sensor and a specific ML model. While this technique is in a development stage, our findings demonstrate that our body-fixed sensor prototype alongside a fully trained validated algorithm can strategically support military operations and offer valuable information to commanders controlling operations remotely. Further stages of our developments include the validation of our refined technique on a larger range of military activities and groups by combining activity data with physiological variables to predict phenomena relating to the onset of fatigue and performance decline.

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Predeployment predictors of psychiatric disorder‐symptoms and interpersonal violence during combat deployment

Abstract: Actuarial models could be used to identify high risk soldiers either for exclusion from deployment or preventive interventions. However, the ultimate value of this approach depends on the associated costs, competing risks (e.g. stigma), and the effectiveness to-be-determined interventions.

Cited by 23 publications

References 44 publications

Pre-deployment risk factors for PTSD in active-duty personnel deployed to Afghanistan: a machine-learning approach for analyzing multivariate predictors

Pre-deployment risk factors for PTSD in active-duty personnel deployed to Afghanistan: a machine-learning approach for analyzing multivariate predictors

Applicability of an Automated Model and Parameter Selection in the Prediction of Screening-Level PTSD in Danish Soldiers Following Deployment: Development Study of Transferable Predictive Models Using Automated Machine Learning

Employing body-fixed sensors and machine learning to predict physical activity in military personnel

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